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Abstract

Although epidemiology is increasingly contributing to policy debates on issues of
conflict and human rights, its potential is still underutilized. As a result, this
article calls for greater collaboration between public health researchers, conflict
analysts and human rights monitors, with special emphasis on retrospective, population-based
surveys. The article surveys relevant recent public health research, explains why
collaboration is useful, and outlines possible future research scenarios, including
those pertaining to the indirect and long-term consequences of conflict; human rights
and security in conflict prone areas; and the link between human rights, conflict,
and International Humanitarian Law.

Introduction

In fall 2006, a controversial estimate of Iraqi war deaths published in the Lancet [1] made world headlines, spurring a renewed round of debate over the ethics and consequences
of the US-led Iraq invasion. The survey found that some 650,000 Iraqis were likely
to have died as a result of the insurgency and sectarian strife following the 2003
invasion. The political ramifications of this figure were undeniable, given US leaders'
insistence that their invasion had been, in part, motivated by humanitarian considerations
[2]. Yet the report also became a magnet for critics, with many questioning the study's
baseline assumptions, sampling methods, and data reporting procedures [3-6].

Methodological criticisms aside, the Lancet-inspired media furor clearly heralded the growing impact of public health research
on conflict and human rights analysis. In particular, it drew attention to the capacity
of "conflict epidemiologists" to provide science-based estimates of the direct and indirect cost of war. Most importantly, perhaps, these epidemiologists are gradually demonstrating
that most existing studies grievously under-estimate war's overall human cost by failing
to capture its indirect and long-term impacts [7-11]. From a human rights perspective, moreover, the legal liability of the commanders
and politicians responsible for this collateral damage remains uncharted territory.

Epidemiological studies can also generate important evidence for policy decisions,
as witnessed in the case of the Democratic Republic of Congo (DRC), where surveys
by the International Rescue Committee (IRC) have called attention to the country's
ongoing humanitarian crisis by discovering vast numbers of indirect, war-related deaths
[12-15]. Of 3.9 million excess deaths from 1998 to 2004, according to these surveys, only
a small proportion have been directly related to political violence, with the remainder
attributed to war-related ailments, such as disease. These findings have proved influential
in policy circles, boosting the conflict's international profile while enhancing the
resources available to peacekeepers and aid workers alike [comments by representatives
from the IRC, Human Rights Watch, and Catholic Relief Services, at workshop on "Integrating
Public Health Methods and Data into Conflict Analysis," Ottawa, March 9, 2007].

Public health research has also helped assess harmful policy impacts short of armed
conflict. One case in point is the 1999 study by a Columbia University researcher
on the link between sanctions and Iraqi child mortality, which revised previous estimates
downwards while still confirming that childhood mortality had risen at alarming rates
[16]. These and other findings, according to one UN insider, played a key role in curbing
the international body's appetite for comprehensive embargoes [comment by Andrew Mack
(former head of strategic planning for the UN Secretary General), at workshop on "Integrating
Public Health Methods and Data into Conflict Analysis," Ottawa, March 9, 2007].

Finally, consider another 1999 study of mortality, this time in the Serbian province
of Kosovo, which argued that 12,000 people likely died during the conflict between
Serbia, NATO and the Kosovo Liberation Army [17]. By mapping trend data against key political and military events, the report demonstrated
that Serbian military activities, rather than NATO air strikes, were correlated with
spikes in mortality. This study broke new ground by linking survey research to international
humanitarian law (IHL, or the "laws of war"), and its findings have found their way
into deliberations at the International Criminal Tribunal for the former Yugoslavia
[18]. More than any other study, perhaps, this analysis illustrates the common interests
of scientifically rigorous public health researchers, policy-oriented conflict analysts,
and human rights monitors, underlining the value-added by multi-disciplinary research.

Although not all public health research is of equal quality, this article argues that
closer collaboration is likely to continue to benefit epidemiologists, conflict analysts,
and human rights monitors. Until now, public health's specialized methods, logistical
complexities, and high costs have hindered multi-disciplinary research, and many non-specialists
only dimly perceive opportunities for collaborative efforts. Many public health specialists,
moreover, have a hard time demonstrating their value-added to lay audiences. This
article seeks to bridge this gap by outlining epidemiology's utility for policy-makers,
conflict analysts, and human rights monitors. For maximum effect, it should be read
by non-epidemiologists in conjunction with a more technically oriented epidemiological
primer [19,20].

Epidemiology's potential contribution

Epidemiology is the statistical study of the distribution of health events, outcomes
and risk factors. This paper focuses on one particular research tool: retrospective
population-based surveys. This emphasis is not intended to identify epidemiology exclusively
with survey methods, but reflects surveys' particular utility for assessing conflict
and human rights impacts. Although real-time and accurate surveillance data from health
care facilities often provide the best measures of current conditions, such data are
rarely available in crisis zones. Retrospective surveys, which ask people to recall
health events during a specified time frame, are a good way of bridging this gap.
Epidemiology has no monopoly on survey research, and human rights groups also use
retrospective methods; most, however, do not rely on population-based techniques.

Since it is rarely possible to survey an entire population (as in a census), researchers
typically question samples that are, in theory, representative of larger groups. When
samples are well designed, their measured characteristics should be similar to those
of the population from which they are drawn. A retrospective survey thus involves
a standardized, structured questionnaire about past events, an accepted sampling method,
and a statistical procedure for inferring about the general population from the sample's
findings. (The appendix provides a more detailed discussion of survey methodology.)

Historically, the field of conflict epidemiology emerged from public health research
in humanitarian emergencies [presentation by Bradley Woodruff, at workshop on "The
Epidemiology of Complex Emergencies," Ottawa, March 8, 2007]. Beginning in the early
1970s, researchers increasingly realized that epidemiology could help devise needs-based
policies and make humanitarian assistance more effective, and in the 1980s and 1990s,
epidemiological findings were used to create general recommendations for improving
the effectiveness of humanitarian assistance. As a result, practitioner manuals have
increasingly included guidelines on using epidemiological methods in humanitarian
assessments. In recent years, experts have developed minimum standards for humanitarian
aid [21] while standardizing epidemiological methods for assessing key indicators [22].

As noted above, conflict epidemiology has also proved relevant to general foreign
policy debates, particularly those pertaining to the creation, deployment and effectiveness
of humanitarian, peacekeeping and peace-building interventions. The quantification
of sickness and death is a concrete measurement of the quality of life (or lack thereof)
in insecure zones, and is easily understood by most policymakers, even if the specific
research methods are not.

Although epidemiology is only one of several quantitative methodologies which can
and should be used in assessing conflict and human rights conditions, it has great
potential due to its ability to offer detailed knowledge about what is happening to
people in conflict, and the immediate causes of those events. It can establish numbers
and rates of health events within populations, and, importantly, identify risk factors
for such outcomes in specific times and places. In sufficiently large samples, moreover,
these data can be broken down by age, gender, ethnicity, caste, or region.

Health data is often of "dual use," informing both evidence-based humanitarian programs
[23], as well as general policy, media and advocacy purposes. Crucially, epidemiological
findings can be particularly useful to human rights monitors, since specific health
risk factors may also be violations of human rights law or the laws of war. With proper
sampling methods, epidemiology can give monitors a more accurate sense of how widespread
particular violations may be, and when trend data are available, researchers can correlate
health outcomes with key political, legal, or military events.

NGO conflict analysis and human rights monitoring

Despite these clear advantages, population-based methods are rarely used by influential
conflict analysis and human rights NGOs. Although the Boston-based Physicians for
Human Rights (PHR) does a superb job of combining population-based surveys and human
rights questions (see below), it is a small player in comparison to major NGOs such
as Human Rights Watch (HRW) or the International Crisis Group (ICG). In 2005, according
to annual reports, PHR's total budget was about US $4 million, compared to $11.4 and
$26 million for the ICG and HRW, respectively. In 2006, moreover, PHR's 46 staffers
were outnumbered by the ICG's 110 and HRW's 233. As a result of these discrepancies
in size, PHR's comparative media impact is small. A keyword search of NGO names in
the Factiva database, for example, found that in 2006, PHR was mentioned by The New York Times only seven times, compared to 63 and 157 for the ICG and HRW, respectively. These
latter two NGOs are leading voices in global policy debates, and their research and
advocacy is often considered "state of the art." To illustrate the value-added of
collaborative research, we critically survey a small and non-random sample of HRW's
and ICG's work.

HRW's and ICG's fieldwork is done at comparatively low cost, often with a slimmer
field presence than epidemiological surveys would require. HRW typically sends a handful
of researchers from its offices to record testimonies, often without explicit government
permission. ICG staffers tend to be based more frequently within their countries of
interest, but their research is similarly unobtrusive. Both groups rely on lengthy,
unstructured interviews, but ICG's researchers focus more heavily on broader political
and governance structures, while HRW concentrates on human rights violations. HRW
generates new information on human rights abuses, but ICG sees its value-added as
one of analysis and prescription.

Problems of data use

Although reports written by the ICG and HRW are compelling, accessible, and effective,
they would be even more powerful were they to rely on methodological input from public
health researchers and other data experts. Consider a 2005 ICG report on forced urban
displacements in Zimbabwe, which cited UN estimates of 700,000 displaced, and 2.4
million indirectly affected individuals. Although the ICG reported that its own "extensive
research ... unearthed no basis for disagreement" with the UN data, it provided little
information on either organization's methods [24]. The UN report itself, disappointingly, is similarly vague [25]. Further inquiry into the UN's data collection, as well as more reflection on the
ICG's own research methods, would have strengthened the Zimbabwe report considerably.
Had the ICG wanted to go one step further, moreover, it could have investigated the
UN data in greater detail, examining its methods to ascertain whether the numbers
were reliable and valid. Or it might have collaborated with survey researchers to
generate new data on the forced displacement, including information on the health
conditions of its victims. Did childhood disease climb after displacement? What was
the displacement's impact on livelihoods, gender-based violence, and other key variables?
This kind of information could have added much to our knowledge of the Zimbabwean
displacement's impacts.

Consider also the ICG's 2006 report on Sri Lanka, which provided no source at all
for its claim of "at least 70,000" having died in the country's north east over the
course of the conflict, or for its assertion that over 2,500 persons had been killed
since hostilities re-ignited in January 2006 [26]. Proper attribution and reflection on data quality are vital, especially for a widely
read organization such as the ICG.

Similar problems are encountered in HRW's reports. Consider the group's 1995 account
of violations of the laws of war in Turkey's Kurdish southeast, written by one of
this paper's co-authors [27]. Although no widely accepted figures existed at the time, the report made use of
a reputable local NGO's claim of two million displaced persons. It made no independent
evaluation of that group's research methods, however, and presented few details for
others to assess. The report also offered little sense of the displacement's impact
on villagers' lives. For example, what effects did forced migration have on their
health? Did they display high levels of mental trauma? Were they more likely to suffer
from disease or child mortality? Retrospective surveys would have given readers and
the Turkish public a better sense of the counterinsurgency's civilian impact.

Ten years later, HRW revisited the issue with a report disputing official figures
on the extent of villagers' return [28]. The study was a laudable effort to delve into the nitty-gritty of official data,
highlighting HRW's growing interest in the mechanics of quantitative work. To dispute
the government's figures, HRW researchers visited several returnee villages, comparing
local accounts of the extent of return to those of the government. Actual return figures,
HRW found, were far lower than those claimed by the government.

Problems of data collection

Yet while the 2005 report on Turkey was persuasive, HRW's evaluation of government
statistics would have been further strengthened by more attention to sampling detail.
For example, the 2005 report gave no information on how HRW researchers selected their
village sample, saying only that researchers "visited a small sample of villages and
hamlets" in three southeastern provinces [28]. As a result, its findings' broad applicability is difficult to assess. To address
this problem, HRW might have visited a random sample of Kurdish villages drawn from
an existing list of depopulated communities, and if that effort proved too laborious,
the group could have used other accepted sampling techniques to select provincial
village clusters, weighted by provincial population size. These and other methods
would have strengthened the credibility and precision of the group's findings.

Unlike the ICG, HRW regularly generates entirely new data based on witness and victim
testimony. The group's careful, one-on-one interviews are regarded as state of the
art by human rights monitors, reducing potential bias through repeated probes and
cross-validation. Yet many HRW interviews are carried out under adverse conditions,
pushing its researchers to rely on non-random convenience samples, while in other
cases, HRW builds its arguments around individual and noteworthy incidents. Both techniques
are problematic. Purposive samples are useful for exploratory research and hypothesis
building, and worst-case documentation is important for moral, advocacy and legal
reasons. Neither, however, is well-suited to establishing a condition's overall prevalence.
In seeking to move from samples to broader generalizations, HRW could usefully draw
on the advice of epidemiologists and other quantitative researchers.

Consider HRW's 2005 report on Nigerian police brutality, which presented powerful
testimonies from 50 persons abused in police custody over the previous four years.
The report left little doubt that something was badly amiss in Nigeria's criminal
justice system. Yet the report argued that "torture and other cruel, inhuman and degrading
treatment by the Nigerian Police Force ... [is] widespread and routine," while simultaneously
acknowledging that its researchers had focused "on a limited number of locations and
cases" [29]. Respondents were interviewed in three separate regions of the country, but the report
gave few details on how HRW researchers had chosen to interview these, as opposed
to other, victims.

To strengthen the report's reliability, HRW might have adapted a standard sampling
procedure. For example, HRW researchers might have taken lists from local Nigerian
Bar Associations to generate a representative sample of defense attorneys in different
regions, and these might have supplied HRW with names of recent clients willing to
be interviewed. Although this procedure would have introduced some bias – not all
detainees would be willing to speak to HRW, while others might not have access to
lawyers – it would still have been a far more systematic approach to assessing the
extent of Nigerian police brutality.

In adopting population-based techniques, however, HRW would have had to interview
Nigerians whose police experience had been satisfactory, requiring a re-allocation
of resources away from worst-case scenarios. Yet HRW, like most human rights groups,
resists spending time and money on interviews with people who had no problems to report.
Surveys, by contrast, are often obliged to expend enormous energies documenting a
problem's non-existence. In the 2004 IRC study of mortality in DRC, for example, surveyors
working on the International Rescue Committee study visited 19,500 households throughout
the country, finding 4000 deaths in a 16-months recall period [15]. Although this finding implied an extraordinarily high national mortality rate, it
also forced researchers to document far more absences of death than actual deaths. It is not clear whether a human rights organization such
as HRW will be willing to use scarce resources in this fashion, even if the payoff
is greater precision and credibility.

A final detailed example will suffice to illustrate the usefulness of collaboration.
In 2006, soon after the end of hostilities, HRW produced a preliminary report on violations
of the laws of war during the Israel-Hezbollah conflict in Lebanon [30], as well as two subsequent and more detailed reports on violations by Hezbollah and
Israel [31,32]. The laws of war limit the right of belligerents to cause civilian suffering and
prohibit efforts to destroy objects "indispensable to the survival of the civilian
population" [33]. Incidental loss of civilian life in warfare is expected, but belligerents are obliged
to limit collateral damage as much as they can. Determining the extent of IHL violations
on both sides was a methodologically and legally complex affair. Both sides had rained
thousands of rockets and shells on the opposite of the border, and both claimed that
they were firing at legitimate military targets.

Given the political sensitivities involved, it is not surprising that HRW's analysis
of Israeli violations attracted the most critical attention. At some level, of course,
Israel's entire military effort in the summer of 2006 could have been regarded as
illegal, since it destroyed so much Lebanese infrastructure while emptying such large
swathes of civilian territory. HRW's analysis of IHL violations typically requires
far greater precision, however, including sophisticated arguments about the legality
of individual air and artillery strikes.

To determine whether particular Lebanese civilian deaths were the result of IHL violations,
HRW had to first establish whether particular Israeli attacks were unlawful. The international
legal principle of distinction holds that belligerents must distinguish between civilians and combatants, while that
of proportionality demands force to be proportional and necessary. A careful IHL study, therefore, required
painstaking, post-hoc reconstructions of Hezbollah activities in the target areas
through conversations with witnesses and other informants, combined with nuanced analyses
of Israeli intentions, capabilities and actions.

To conduct its study, HRW assembled lists of Israeli attacks that resulted in Lebanese
civilian casualties. It then read media reports and spoke to key informants, seeking
to determine which events allegedly involved indiscriminate fire, and then targeted
this subset for more detailed field research. For the larger and more detailed 2007
report [32], HRW investigated the circumstances surrounding 561 (500 civilians and 61 combatants)
of a total 1,109 Lebanese killed by Israeli fire. Almost 60% of the civilians killed,
according to the HRW study, died as a result of unlawful Israeli strikes. As a result,
HRW concluded that Israeli forces had systematically violated IHL [personal email
correspondence with Iain Levine (HRW), September 20, 2007].

There is little question that HRW's research on this count was laudable, assembling
important data under difficult conditions. Yet a population-based approach might have
added still greater precision, helping HRW discern with even greater confidence whether
Israeli violations had been both routine and widespread during the 2006 summer war.

For example, HRW might have first worked with local Lebanese authorities, medical
workers and others to generate a reasonably comprehensive list of all communities
targeted by Israeli fire (rather than just those where civilians died). Next, HRW
might have sought to determine which of those communities had experienced civilian
casualties. From this subset, HRW researchers could have then selected a representative
sample, using accepted sampling techniques, for detailed field investigation. This
sequence might have helped HRW better estimate the proportion of Israeli attacks involving
IHL violations. The data could have then been disaggregated by time and region, giving
a better sense of Israeli violations across time and space. This information, in turn,
would have helped determine with greater precision the nature of Israeli culpability.
For example, a small number of criminal attacks would suggest localized problems of
coordination and control, while larger and more consistent patterns would indicate
higher-level intentionality.

Finally, the report could have been usefully supplemented with health surveys or surveillance
data from hospitals and clinics. With the help of public health specialists, HRW could
have gained a better sense of the overall civilian impacts of Israel's campaign, which
destroyed much of southern Lebanon's transportation infrastructure, homes and businesses.
What, for example, were the maternal, child, and general mortality trends in the six
months following Israel's campaign? Governments and armed groups should be held accountable
for these indirect damages, an issue we return to below.

What do conflict epidemiologists do?

Conflict epidemiologists are particularly concerned with conditions during and after
complex emergencies, defined as "relatively acute situations affecting large civilian
populations, usually involving a combination of war or civil strife, food shortages
and population displacement, resulting in significant excess mortality" [34]. Mortality is a key indicator of overall population health [35], but epidemiologists may also seek information about a range of other health indicators,
including morbidity, malnutrition, sanitation, and access to health care. Importantly,
mortality is methodologically easier to assess than other health indicators. As noted
above, mortality studies are important for assessing the direct and indirect impact
of conflict, but this section also discusses other epidemiological research efforts
relevant to conflict and human rights analysis.

The causes and conditions of displacement

In some epidemiological studies, such as the Iraq Lancet study discussed above, epidemiologists survey national populations. Most conflict epidemiology, however, focuses on more compact and survey-able
groups such as refugees or displaced persons. As a result, there is a dearth of good
information about health conditions outside of clearly delineated population centers.
Retrospective questionnaires can help address this gap by asking refugees or displaced
persons about conditions prior to, and during, flight.

In Darfur, for example, survey researchers questioned villagers about events before,
during, and after displacement, learning much about human rights and health conditions
in inaccessible regions [36]. The surveys revealed that most respondents fled from militia violence, and that
violent causes of death, rather than disease or hunger, predominated in the "village
and flight" period. Once respondents reached organized camp locales, however, medical
causes of death predominated, suggesting that respondents were largely safe from direct
military violence. Thus even when it proves impossible to survey Darfur's interior
regions, researchers can use retrospective surveys in safe peripheral areas to gather
vital, science-based information on events in inaccessible zones.

Consider also a 1999 PHR survey in Macedonia and Albania among ethnic Albanian refugees
fleeing Kosovo during the conflict between Serbia, NATO and the Kosovo Liberation
Army. This study sought information on the time frame and reasons for displacement,
and on experiences of human rights abuses. PHR could not send surveyors into Kosovo
at the time, but surveys of refugees provided strong evidence that Serb forces had
engaged in a systematic expulsion campaign [37].

The civilian impacts of munitions and military tactics

Munitions impact studies are another powerful application of retrospective surveys.
In a 1995 Mozambique study, for example, researchers found rates of landmine-related
death and injury far in excess of those suggested by prospective surveillance methods
[38], while in a larger study of landmine impacts in Afghanistan, Bosnia, Cambodia, Mozambique,
surveyors found that six percent of households suffered landmine victims, and that
25–87% suffered landmine-related impacts [39]. In this case, retrospective surveys thus shed important light on the utility of
a global landmine ban.

Such studies may also have important spill-over effects. In Afghanistan, for example,
a study of landmine and unexploded ordnance impacts helped researchers launch a key
informant strategy for estimating civilian deaths over large areas [40]. Using various data sources, surveyors visited all 747 Afghani communities suspected
of having endured a coalition air or ground attack, finding 600 that had actually
experienced hostilities. Rather than using a household survey, however, researchers
elected to question local key informants in each community, with counterintuitive
results: 43% of communities reported no direct-violence victims, while 66% had no
landmine or unexploded ordnance deaths. Civilian casualties, in other words, were
tightly clustered in a smaller number of locales, a finding the authors interpreted
by differentiating between the impacts of air and ground attacks. NATO air raids appeared
to scatter Taliban forces, leading to fewer civilian casualties; NATO ground attacks
against Taliban fighters who held their ground or regrouped, by contrast, led to more
civilian deaths.

The policy implications of this study were wide ranging; not only did it find that
5,576 Afghanis had been killed and 5,194 injured from September 2001 to June 2002,
but it also shed light on the way in which these individuals had died. Methodologically, the study broke new ground
by combining comprehensive key informant interviews with statistical techniques. The
study located informants in all violence-affected communities, seeking to determine patterns and causes of death and
injury. Although key informants may be biased by political affiliation or the desire
for aid, the method's broad geographic coverage has clear practical and methodological
advantages.

Conflict-related morbidity

Epidemiologists also seek to estimate the effect of conflict on disease by using retrospective
mortality studies called "verbal autopsies." The International Rescue Committee's
surveys in the Democratic Republic of Congo, for example, found that infectious disease
was the country's biggest killer, far outstripping direct conflict deaths and injury.
Cross-national analysis of summary disease data has also found that civil wars greatly
increase the risk of infectious disease [7]. The most important immediate causes of deaths in complex emergencies are acute respiratory
infections, diarrheal diseases, maternal and neonatal morbidity, tuberculosis, and
vector-borne diseases such as malaria. Disease risk is increased by several conditions
common in complex emergencies, including overcrowding and inadequate shelter; malnutrition;
insufficient vaccination; poor water and sanitation conditions; exposure to "new"
diseases, for which affected populations have not developed immunity; and lack of,
or delay in, treatment [41]. In recent years, researchers have also become concerned with the effect of conflict
on particular communicable diseases, such as HIV-AIDS, but the links in this case
remain contested [personal telephone communication with Paul Spiegel (UNHCR), December
5, 2006].

Conflict-related mental health

Another use of population-based surveys lies in assessing the impact of complex emergencies
on mental health. Although this remains a comparatively neglected area of study, the
existing evidence suggests, not surprisingly, that mental illnesses increase in emergency
settings, and that multiple human rights violations may have cumulative and negative
mental health impacts [41]. Like indirect conflict mortality, adverse mental health impacts are part of a conflict's
overall human costs, and should be factored into broader impact assessments.

Mental health impacts can also have important political consequences. Consider, for
example, one study of links between traumatic experiences during the 1994 Rwandan
genocide and attitudes towards post-conflict justice. Nearly a quarter of respondents
displayed PTSD symptoms, and they were less likely to have positive attitudes toward
the Rwandan national trials and interdependence with other ethnic groups. Furthermore,
persons who experienced multiple traumatic events were more likely to have positive
attitudes toward the International Criminal Tribunal for Rwanda but less likely to
support national and local justice and reconciliation processes [42]. Consider also a study of mental health and attitudes among Kosovar Albanians following
the 1998–99 war, which revealed an association between traumatic war time events,
decreased mental health, impaired social functioning, and strong respondent emotions
of hatred and revenge toward Serbs [43].

The extent and scope of human rights abuses

As noted above, PHR has pioneered efforts to use population-based surveys in assessing
the extent of human rights violations. In a number of cases, PHR's efforts have yielded
important results. Research on sexual violence, for example, is inherently difficult
[44]; PHR's 2002 report on the experiences of displaced persons in Sierra Leone, however,
successfully produced a wealth of important data with the help of the local UN mission,
trained local staff, and carefully designed surveys [45,46]. Seventeen percent of respondents in displaced person camps reported at least one
lifetime sexual assault, while nine percent reported an assault during the war. And
while this number appeared low given media reports of widespread sexual violence during
Sierra Leone's civil war, PHR's survey established that the main rebel group, the
Revolutionary United Front (RUF), was systematically committing sexual abuse. According
to the study, 53% of the women reporting direct, face-to-face contact with RUF fighters
also reported that they had been sexually assaulted, compared to less than six percent
for those exposed to other combatant groups. As a result of these and other findings,
the 2002 PHR report played a key role in Sierra Leone's transitional justice debates,
pushing gender violence to the top of the agenda [47]. Another successful PHR study is its 2000 survey of displaced Chechens, which documented
widespread abuse by Russian forces. In nearly all cases, PHR found, displacement was
attributed to Russian actions, rather than those of Chechen insurgents [48]. PHR has conducted similarly innovative surveys on events in Kosovo (see above),
Afghanistan, and Iraq.

Another example of inter-disciplinary research comes from an innovative Johns Hopkins
team that has found a correlation between human rights violations and specific adverse
health outcomes [49]. At the initiative of local "back-pack" medics working in Burma's eastern border
area, researchers inserted a series of human rights questions into a 2004 health survey.
Of 1,834 surveyed households, 33% reported being subjected to forced labor, nine percent
had been internally displaced, and 25% had food or other essential items stolen or
destroyed by Burmese military forces. With the help of these findings, the team was
able to compare the health of displaced and non-displaced families, finding that the
former were 2.8 times more likely to have experienced a child's death, 3.2 times more
likely to have a malnourished child, and 3.9 times more likely to have suffered a
landmine injury. Those experiencing human rights violations, moreover, were also more
likely to experience child mortality and landmine injury. By correlating specific
health problems to specific abuses, the Johns Hopkins researchers successfully provided
evidence useful to human rights monitors, humanitarian workers, and conflict analysts
alike.

Post-conflict conditions

Population-based surveys have also provided information about conditions in post-conflict
settings. Although peace should theoretically be associated with greater physical
and mental well-being, this is not always true. For example, PHR studied health conditions
in Chiapas, Mexico, years after insurgents ended their armed rebellion [50], and their survey of 2,997 households in 46 communities discovered that health conditions
had in fact deteriorated alarmingly, with some communities being denied healthcare
for political reasons. Thus, while Chiapas' shooting war had ended, health conditions
were in fact getting worse, not better. Unfortunately, researchers may find similar
post-conflict deterioration elsewhere.

At the policy level, these and other findings strongly suggest that the UN and other
agencies should commission immediate post-conflict surveys to establish baseline data
on existing human rights and health conditions. Over time, follow-up studies could
then track improvements, or lack thereof, for specific population segments. This combination
of baseline and follow up research could then give scientists, human rights activists,
and policy makers reliable information on the real impacts of post-conflict arrangements
on public health and well-being.

The limits of population-based surveys

Like any research method, retrospective surveys suffer from limitations, and they
are neither useful nor appropriate for all times and places. For starters, population-based
surveys are logistically complex and costly, requiring local teams of trained researchers,
coordination and supervision. Epidemiology is a highly technical affair, requiring
training and experience in sampling, questionnaire design, interviewing, and statistical
analysis. Given these and other complexities, it is not surprising that experts often
criticize field NGOs' surveys [51]. Keeping up with the methodological state of the art is difficult, and experts continue
to refine accepted techniques. Experts, moreover, constantly debate the most appropriate
methods for different settings [36,52,53]. Complex emergencies vary dramatically, and a one-size-fits-all research method is
not appropriate [35].

Population-based surveys are often difficult to implement in insecure areas, since
both survey teams and respondents are vulnerable and hard to monitor. Governments
or armed groups frequently deny access, making studies difficult where they are needed
the most. Importantly, surveys in politically tense environments can raise thorny
ethical dilemmas by placing both informants and researchers at risk of reprisals or
re-traumatization [54-58]. If epidemiology is increasingly used for human rights analysis and to provide grounds
for external intervention, moreover, governments may begin to block general public
health research among needy populations, to the detriment of humanitarian assistance
programs. [personal email communication with Francesco Checchi, November 6, 2006].
Yet the failure to use powerful research methodologies for advocacy on behalf of vulnerable
populations may itself be unethical [54].

The survey process is also vulnerable to political manipulation from all sides. For
instance, asking respondents about who is responsible for individual deaths is problematic,
as respondents may give false information for a wide variety of personal and political
reasons. Respondents may also not be willing to tell interviewers that members of
their households were combatants. Another ethical issue arises if everyone involved
in a survey, including researchers employed by humanitarian agencies, has an interest
in inflated numbers. For this reason, many experts believe that scientific data collection
and political advocacy should be kept separate to maintain the science's legitimacy
and credibility [participant comments at workshop on "Integrating Public Health Methods
and Data into Conflict Analysis," Ottawa, March 9, 2007]. These issues should not
preclude collaboration between epidemiologists and conflict analysts/human rights
monitors, but they do need to be addressed in the research process.

A final drawback of epidemiological research is that the relevance of its findings
can be difficult to convey to policy-makers and the general public. As the polemic
inspired by the Iraq Lancet study suggests, the media's agenda may focus too heavily on perceived methodological
problems, despite poor understanding of the technicalities involved, and of these
problems' implications for the results' validity. Policy-makers opposed to a given
study's findings will dismiss them as imprecise, while advocates may fail to acknowledge
that their numbers come with biases and substantial margins of error.

Why collaborate?

While epidemiology is a powerful and under-exploited tool, the quantification of suffering
is rarely sufficient, on its own, to ensure action. The political, economic, and logistical
barriers to effective external intervention are well known, while new research has
emerged suggesting that there are also substantial psychological barriers to promoting
better public awareness of, and concern for, mass atrocities [59]. Full exploitation of epidemiology's potential will thus require close collaboration
between public health analysts, conflict researchers, and human rights monitors.

There is little doubt that the research and writing styles of large NGOs, such as
HRW and the ICG, offer important advantages, including unobtrusive research in insecure
areas, and broadly accessible, easy-to-read reports. More importantly, perhaps, their
detailed, confidential interviews with officials and other key informants can help
establish causality in ways that statisticians find hard to emulate. Although epidemiology
can demonstrate correlations, precise causal links are often more easily revealed
through qualitative methods, such as "process tracing" of political decisions, chains
of command, and actors' intentions, which "is fundamentally different from statistical
analysis because it focuses on sequential processes within a particular [...] case,
not on correlations across cases" [60].

While all organizations using data should understand and communicate its limitations,
we are particularly concerned with the work of organizations generating new data,
such as HRW. Like most human rights groups, HRW's ethos is grounded in international
law, and most of its employees are not trained in epidemiology or other quantitative
methods. With limited staff and a host of pressing demands, HRW finds it hard to prioritize
discussions of careful sampling and data collection. Still, the group is constantly
re-examining its research methods, and innovative collaborative efforts are already
underway, including the group's report on abuses in Kosovo, in which data from 577
witnesses was coded and analyzed [61], and its report on Bangladeshi police forces, which charted the distribution of killings
per population across police divisions [62].

Qualitative research groups such as HRW are not likely to transform themselves into
survey outfits in the near future. Still, HRW and other qualitative research groups
can and should become more conscious of their methodological limitations. Conflict
epidemiologists, among others, can help generate more scientifically defensible evidence,
and can also help clarify what the evidence shows, and what it does not. Although
neither HRW nor the ICG have voiced interest in creating an in-house epidemiological
capacity, both have expressed an interest in public health collaboration, including
joint questionnaire design and better use of existing epidemiological results. At
the same time, we discern growing interest among public health researchers in broader
dissemination of their methods and data, and in working with others on the underlying
causes of conflict and human rights abuse [34,63,64]. To be effective, these different research communities should become more literate
in each other's lexicons, and engage in more frequent and respectful collaboration.
The time for new research partnerships has arrived.

In this paper, we have provided a number of examples of public health research with
proven relevance to conflict and human rights analysis. We conclude with a final collaborative
scenario: the application of IHL analysis to the long-term human costs of war. At
present, IHL offers little commentary on the legality of destroying the public infrastructure
necessary for long-term health and human rights, preferring to concentrate on war's
shorter-term and more immediate effects [65]. International lawyers find IHL's proportionality principle particularly hard to
apply over time due to the intervention of complicating factors that make it hard
to link cause and effect. Twelve months after a war's end, how much of a country's
increased infant mortality could realistically – and legally – be attributed to wartime
actions by combatants, as opposed to those of myriad other actors and events?

Given these complexities, human rights groups have hitherto preferred to focus on
shorter time frames, where causality and legal responsibility are easier to establish.
Over time, however, the accumulation of good quality epidemiological data can help
broaden and extend the IHL analysis to longer post-conflict periods. The availability
of relevant information is crucial, since IHL violations are judged on expected losses weighed against anticipated military advantages. As one analysis notes, "it is unacceptable for the expected military
advantage to be based on a longer timeframe while limiting the expected quantification
of civilian damage only to the immediate effects of the attack itself" [66]. By repeatedly documenting the short, medium and long-term impact of specific military
tactics, epidemiological research can force military planners to increase the horizon
of what they can reasonably predict. This argument is already being used in the ongoing
debate over cluster munitions, where some believe IHL requires commanders to consider
the explosives' long-term threat to civilians [66].

Conclusion

Epidemiology is able to provide evidence of human suffering of great value to conflict
analysts and human rights monitors. More often than not, information on the civilian
impacts of conflict is based on informed guesses by NGOs and multilateral organizations,
rather than rigorously assembled scientific data. This paper has identified problems
of data use and collection by two major advocacy NGOs, arguing that these short-comings
are particularly problematic when establishing the overall prevalence of a particular
human rights abuse or conflict pattern. These data gaps, we argue, can be addressed
in part through greater collaboration with public health researchers.

Epidemiology can help quantify the differential direct and indirect impact of conflict
on particular populations, while trend data can track impacts over time, enabling
researchers to map health outcomes against major offensives; peacekeeping operations;
humanitarian assistance flows; and peace agreements. This information can shed light
on the efficacy of international engagement in conflict zones, while providing human
rights investigators with a way of assessing the extent and impact of violations across
populations.

Research collaboration between public health specialists, conflict analysts and human
rights monitors faces practical and ethical difficulties. These should be acknowledged
and addressed, but they should not preclude the kind of collaborative research that
could benefit needy and distressed populations.

Appendix: How are population-based surveys done?

Epidemiological surveys collect quantitative health indicators from populations at
a specific time, using standardized, structured questionnaires. In retrospective surveys,
surveyors ask respondents to recall health events that occurred during a specified
time frame known as the recall period. Although surveys can be exhaustive by including every person in the population (such
as a census), they are usually based on representative samples. When samples are well
designed, their measured characteristics should be similar to those of the population
from which they are drawn. Survey design has to contend with bias (non-sampling error)
and imprecision (sampling error).

Sampling is the selection of a specified number of persons or households from a population.
Epidemiologists usually employ probability sampling, which ensures that every selected person or household has the same known chance
of inclusion. Sample size should be large enough to provide reliable estimates, but
not so large so as to waste limited time and resources. Larger samples are required
for greater statistical precision or to investigate a condition with low prevalence within the population, such as maternal
mortality. There are three general methods of probability sampling.

Simple random sampling requires a complete list of all the units to be sampled, such as households, and a
certain number are then randomly selected from this sampling frame. Although this method is often the most representative, it is rarely feasible in
conflict settings because of the paucity of complete lists. But even if good listings
are available, simple random sampling is generally more expensive as it requires broad
coverage of wide areas, and is thus logistically complex. For these reasons, simple
random sampling is often only used when studying registered populations that are concentrated
in small areas, such as well-organized refugee camps.

Systematic sampling, by contrast, randomly selects only the starting unit; all other units are selected
by adding a certain number (known as the sampling step), which depends on the desired sample size. While this method does not require a
comprehensive list to start, it does need a well-ordered population and a good estimate
of population size, so that the sampling step can be calculated and applied. Again,
systematic sampling is often possible in refugee camps or other well-delineated populations.

A third method, multi-stage cluster sampling, begins by listing clusters of sampling units, such as administrative divisions or
villages, and then randomly selects a certain number of these. Cluster selection must
be proportional to relative population size, so that areas with greater populations
are allocated more clusters. Clusters can be selected at more than one stage of sampling.
At the final stage, variants of the sampling methods outlined above can be used to
select an equal number of households from each cluster. In many cases, the first household
is selected randomly, while the rest are selected by proximity to the first. This
method is a good way of creating representative samples even when there are no adequate
listings of the entire population, or when households are not distributed in an ordered
pattern. To do a good multi-stage cluster sample, one must simply be familiar with
basic geographic divisions and their relative population size. Cluster sampling may
also limit logistical and security concerns by reducing the movement of survey teams
to a few random points. This also makes cluster sampling cheaper than random sampling.
For these reasons, cluster sampling is often the sampling method of choice in complex
emergencies.

Multi-stage cluster sampling has important drawbacks, however. Cluster sampling cannot
be used to analyze quantitative differences between geographic divisions unless the
population is first stratified by relevant criteria, with separate cluster samples
drawn from each stratum. This increases the overall number of clusters needed. Moreover,
statistical precision is lower in cluster samples, since households within clusters
are more likely to resemble each other than if they were selected randomly from the
entire population. This leads to a loss in sampling variability known as the design effect. This is particularly problematic when measuring highly clustered phenomena such
as the effects of violent conflict. To compensate, researchers must increase the sample's
overall size. Since it is statistically preferable to increase the number of clusters
rather than the number of households within clusters, this compensatory adjustment
often boosts the survey's cost and duration.

Samples always come with biases, which should be minimized and acknowledged. To prevent avoidable biases, researchers
must try to ensure that the data they collect closely reflects the respondents' situation.
This requires that the data collection effort be standardized and tightly monitored
for quality. Questionnaires should be simple and clear, and fewer questions generally
provide better measurements. Survey interviewers should be identically trained so
that they do not influence responses.

Competing interests

The author(s) declare that they have no competing interests.

Authors' contributions

ONTT carried out the literature review, participated in the design of the study, drafted
the initial manuscript, and is principal author. JR conceived of the study, obtained
the funding, coordinated the workshop, designed the study and manuscript structure,
contributed some sections, and edited the manuscript. Both authors participated in
revisions and read and approved the final text.

Acknowledgements

This research was funded by Human Security Program Grant #06-191, Department of Foreign
Affairs and International Trade, Canada (DFAIT); the Social Sciences and Humanities
Research Council; and the International Development Research Centre. We are grateful
to Valerie Percival and Gregg Greenough for input, to Aimee Charest for research assistance,
and to Gaya Sanmugam for administrative support. We also thank Richard Garfield, Paul
Spiegel, Jennifer Leaning, Iain Levine, Sam Zia-Zarifi, Robert Temple, and other participants
at the DFAIT-funded 9 March 2007 workshop, "Integrating Public Health Methods and
Data into Conflict Analysis."

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